vCBIR: A Verifiable Search Engine for Content-Based Image Retrieval

Shangwei Guo, Yang Ji, Ce Zhang, Cheng Xu, and Jianliang Xu

Abstract

We demonstrate vCBIR, a verifiable search engine for Content-Based Image Retrieval. vCBIR allows a small or medium-sized enterprise to outsource its image database to a cloud-based service provider and ensures the integrity of query processing. Like other common data-as-a-service (DaaS) systems, vCBIR consists of three parties: (i) the image owner who outsources its database, (ii) the service provider who executes the authenticated query processing, and (iii) the client who issues search queries. By employing a novel query authentication scheme proposed in our prior work, the system not only supports cloud-based image retrieval, but also generates a cryptographic proof for each query, by which the client could verify the integrity of query results. During the demonstration, we will showcase the usage of vCBIR and also provide attendees interactive experience of verifying query results against an untrustworthy service provider through graphical user interface (GUI).
Type
Conference paper
Publication
In Proceedings of the 36th IEEE International Conference on Data Engineering (ICDE ’20)
Date
April 2020
Note
Demo Paper, accepted to appear
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